Building Secure and Responsible LLM Applications Training Course
Security in LLM applications involves the practice of designing, constructing, and sustaining safe, reliable, and policy-adherent systems that leverage large language models.
This instructor-led, live training (available online or onsite) targets AI developers, architects, and product managers at intermediate to advanced levels. The course focuses on identifying and mitigating risks inherent to LLM-powered applications, such as prompt injection, data leakage, and unfiltered outputs. Participants will learn to implement security controls like input validation, human-in-the-loop oversight, and output guardrails.
Upon completion of this training, participants will be able to:
- Comprehend the fundamental vulnerabilities of LLM-based systems.
- Apply secure design principles to the architecture of LLM applications.
- Utilize tools like Guardrails AI and LangChain for validation, filtering, and ensuring safety.
- Incorporate techniques such as sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Customization Options
- For those interested in a customized training session for this course, please contact us to arrange details.
Course Outline
Overview of LLM Architecture and Attack Surface
- How LLMs are built, deployed, and accessed via APIs.
- Key components in LLM app stacks (e.g., prompts, agents, memory, APIs).
- Where and how security issues arise in real-world use.
Prompt Injection and Jailbreak Attacks
- What is prompt injection and why it’s dangerous.
- Direct and indirect prompt injection scenarios.
- Jailbreaking techniques to bypass safety filters.
- Detection and mitigation strategies.
Data Leakage and Privacy Risks
- Accidental data exposure through responses.
- PII leaks and model memory misuse.
- Designing privacy-conscious prompts and retrieval-augmented generation (RAG).
LLM Output Filtering and Guarding
- Using Guardrails AI for content filtering and validation.
- Defining output schemas and constraints.
- Monitoring and logging unsafe outputs.
Human-in-the-Loop and Workflow Approaches
- Where and when to introduce human oversight.
- Approval queues, scoring thresholds, fallback handling.
- Trust calibration and role of explainability.
Secure LLM App Design Patterns
- Least privilege and sandboxing for API calls and agents.
- Rate limiting, throttling, and abuse detection.
- Robust chaining with LangChain and prompt isolation.
Compliance, Logging, and Governance
- Ensuring auditability of LLM outputs.
- Maintaining traceability and prompt/version control.
- Aligning with internal security policies and regulatory needs.
Summary and Next Steps
Requirements
- A solid understanding of large language models and prompt-based interfaces.
- Experience in developing LLM applications using Python.
- Familiarity with API integrations and cloud-based deployments.
Target Audience
- AI developers.
- Application and solution architects.
- Technical product managers utilizing LLM tools.
Open Training Courses require 5+ participants.
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